{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_StoreAction(option_strings=['--train_ratio'], dest='train_ratio', nargs=None, const=None, default=1, type=<class 'float'>, choices=None, help='the ratio of the data used in meta-training.                     set train_ratio<1 to use a random subset for meta-training (default: 1)', metavar='N')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from mesa import Mesa\n",
    "import argparse\n",
    "from utils import Rater, load_dataset\n",
    "from sklearn.tree import *\n",
    "from copy import deepcopy\n",
    "import time\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "parser = argparse.ArgumentParser(description='Mesa Arguments')\n",
    "parser.add_argument('--env-name', default=\"MESA-SAC\")\n",
    "\n",
    "# SAC arguments\n",
    "parser.add_argument('--policy', default=\"Gaussian\",\n",
    "                    help='Policy Type: Gaussian | Deterministic (default: Gaussian)')\n",
    "parser.add_argument('--eval', type=bool, default=True,\n",
    "                    help='Evaluates a policy every 10 episode (default: True)')\n",
    "parser.add_argument('--gamma', type=float, default=0.99, metavar='G',\n",
    "                    help='discount factor for reward (default: 0.99)')\n",
    "parser.add_argument('--tau', type=float, default=0.01, metavar='G',\n",
    "                    help='target smoothing coefficient(τ) (default: 0.01)')\n",
    "parser.add_argument('--lr', type=float, default=0.001, metavar='G',\n",
    "                    help='learning rate (default: 0.001)')\n",
    "parser.add_argument('--lr_decay_steps', type=int, default=10, metavar='N',\n",
    "                    help='step_size of StepLR learning rate decay scheduler (default: 10)')\n",
    "parser.add_argument('--lr_decay_gamma', type=float, default=0.99, metavar='N',\n",
    "                    help='gamma of StepLR learning rate decay scheduler (default: 0.99)')\n",
    "parser.add_argument('--alpha', type=float, default=0.1, metavar='G',\n",
    "                    help='Temperature parameter α determines the relative importance of the entropy\\\n",
    "                            term against the reward (default: 0.1)')\n",
    "parser.add_argument('--automatic_entropy_tuning', type=bool, default=False, metavar='G',\n",
    "                    help='Automaically adjust α (default: False)')\n",
    "parser.add_argument('--seed', type=int, default=None, metavar='N',\n",
    "                    help='random seed (default: None)')\n",
    "parser.add_argument('--batch_size', type=int, default=64, metavar='N',\n",
    "                    help='batch size (default: 64)')\n",
    "parser.add_argument('--hidden_size', type=int, default=50, metavar='N',\n",
    "                    help='hidden size (default: 50)')\n",
    "parser.add_argument('--updates_per_step', type=int, default=1, metavar='N',\n",
    "                    help='model updates per simul|ator step (default: 1)')\n",
    "parser.add_argument('--update_steps', type=int, default=1000, metavar='N',\n",
    "                    help='maximum number of steps (default: 1000)')\n",
    "parser.add_argument('--start_steps', type=int, default=500, metavar='N',\n",
    "                    help='Steps sampling random actions (default: 500)')\n",
    "parser.add_argument('--target_update_interval', type=int, default=1, metavar='N',\n",
    "                    help='Value target update per no. of updates per step (default: 1)')\n",
    "parser.add_argument('--replay_size', type=int, default=1000, metavar='N',\n",
    "                    help='size of replay buffer (default: 1000)')\n",
    "parser.add_argument('--cuda', action=\"store_true\", default=False,\n",
    "                    help='run on CUDA (default: False)')\n",
    "\n",
    "# MESA arguments\n",
    "parser.add_argument('--dataset', type=str, default='Mammo', metavar='N',\n",
    "                    help='the dataset used for meta-training (default: Mammo)')\n",
    "parser.add_argument('--metric', type=str, default='aucprc', metavar='N',\n",
    "                    help='the metric used for evaluate (default: aucprc)')\n",
    "parser.add_argument('--reward_coefficient', type=float, default=100, metavar='N')\n",
    "parser.add_argument('--num_bins', type=int, default=5, metavar='N', \n",
    "                    help='number of bins (default: 5). state-size = 2 * num_bins.')\n",
    "parser.add_argument('--sigma', type=float, default=0.2, metavar='N', \n",
    "                    help='sigma of the Gaussian function used in meta-sampling (default: 0.2)')\n",
    "parser.add_argument('--max_estimators', type=int, default=10, metavar='N',\n",
    "                    help='maximum number of base estimators in each meta-training episode (default: 10)')\n",
    "parser.add_argument('--meta_verbose', type=int, default=10, metavar='N',\n",
    "                    help='number of episodes between verbose outputs. \\\n",
    "                    If \\'full\\' print log for each base estimator (default: 10)')\n",
    "parser.add_argument('--meta_verbose_mean_episodes', type=int, default=25, metavar='N',\n",
    "                    help='number of episodes used for compute latest mean score in verbose outputs.')\n",
    "parser.add_argument('--verbose', type=bool, default=False, metavar='N',\n",
    "                    help='enable verbose when ensemble fit (default: False)')\n",
    "parser.add_argument('--random_state', type=int, default=None, metavar='N', \n",
    "                    help='random_state (default: None)')\n",
    "parser.add_argument('--train_ir', type=float, default=1, metavar='N', \n",
    "                    help='imbalance ratio of the training set after meta-sampling (default: 1)')\n",
    "parser.add_argument('--train_ratio', type=float, default=1, metavar='N', \n",
    "                    help='the ratio of the data used in meta-training. \\\n",
    "                    set train_ratio<1 to use a random subset for meta-training (default: 1)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialization and meta-training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' Prepare the Environment '''\n",
    "\n",
    "#  load dataset\n",
    "dataset = 'Mammo'\n",
    "X_train, y_train, X_valid, y_valid, X_test, y_test = load_dataset(dataset)\n",
    "estimator, base_estimator = 'DT', DecisionTreeClassifier(max_depth=None)\n",
    "args = parser.parse_args([])\n",
    "n_estimators = args.max_estimators\n",
    "\n",
    "# plot the class distribution\n",
    "def plot_class_distribution(ax, labels, title):\n",
    "    sns.countplot(data=pd.DataFrame(labels, columns=['Class']), x='Class', ax=ax)\n",
    "    ax.set(title=title)\n",
    "   \n",
    "sns.set(style='whitegrid')\n",
    "sns.set_context('talk', font_scale=1)\n",
    "fig, ax = plt.subplots(figsize=(20, 6))\n",
    "plot_class_distribution( \n",
    "    ax = ax, \n",
    "    labels = np.concatenate([y_train, y_valid, y_test]),\n",
    "    title = f'{dataset} dataset class distribution')\n",
    "plt.tight_layout(pad=1.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: Mammo\n",
      "Start meta-training of MESA ... ...\n",
      "Epi.10   updates 0    |last-25-mean-aucprc| train 0.879 | valid 0.502 | test 0.523 | by rand\n",
      "Epi.20   updates 0    |last-25-mean-aucprc| train 0.863 | valid 0.485 | test 0.509 | by rand\n",
      "Epi.30   updates 0    |last-25-mean-aucprc| train 0.863 | valid 0.491 | test 0.521 | by rand\n",
      "Epi.40   updates 0    |last-25-mean-aucprc| train 0.881 | valid 0.495 | test 0.531 | by rand\n",
      "Epi.50   updates 0    |last-25-mean-aucprc| train 0.916 | valid 0.507 | test 0.545 | by rand\n",
      "Epi.60   updates 40   |last-25-mean-aucprc| train 0.920 | valid 0.513 | test 0.554 | by mesa\n",
      "Epi.70   updates 130  |last-25-mean-aucprc| train 0.954 | valid 0.549 | test 0.604 | by mesa\n",
      "Epi.80   updates 220  |last-25-mean-aucprc| train 0.975 | valid 0.589 | test 0.643 | by mesa\n",
      "Epi.90   updates 310  |last-25-mean-aucprc| train 0.976 | valid 0.609 | test 0.647 | by mesa\n",
      "Epi.100  updates 400  |last-25-mean-aucprc| train 0.976 | valid 0.601 | test 0.650 | by mesa\n",
      "Epi.110  updates 490  |last-25-mean-aucprc| train 0.976 | valid 0.591 | test 0.651 | by mesa\n",
      "Epi.120  updates 580  |last-25-mean-aucprc| train 0.976 | valid 0.595 | test 0.659 | by mesa\n",
      "Epi.130  updates 670  |last-25-mean-aucprc| train 0.976 | valid 0.598 | test 0.663 | by mesa\n",
      "Epi.140  updates 760  |last-25-mean-aucprc| train 0.976 | valid 0.599 | test 0.651 | by mesa\n",
      "Epi.150  updates 850  |last-25-mean-aucprc| train 0.976 | valid 0.600 | test 0.653 | by mesa\n",
      "Epi.160  updates 940  |last-25-mean-aucprc| train 0.976 | valid 0.594 | test 0.642 | by mesa\n",
      "Meta-training time: 16.545 s\n"
     ]
    }
   ],
   "source": [
    "''' Meta-training '''\n",
    "\n",
    "# initialize MESA\n",
    "print ('Dataset: {}'.format(dataset))\n",
    "mesa = Mesa(\n",
    "    args=args, \n",
    "    base_estimator=base_estimator, \n",
    "    n_estimators=args.max_estimators)\n",
    "\n",
    "# start meta-training\n",
    "print ('Start meta-training of MESA ... ...')\n",
    "start_time = time.clock()\n",
    "mesa.meta_fit(X_train, y_train, X_valid, y_valid, X_test, y_test)\n",
    "end_time = time.clock()\n",
    "print ('Meta-training time: {:.3f} s'.format(end_time - start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the meta-training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x252 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def slide_mean(data, window_half):\n",
    "    result = []\n",
    "    for i in range(len(data)):\n",
    "        lower_bound = max(i-window_half, 0)\n",
    "        upper_bound = min(i+window_half+1, len(data)-1)\n",
    "        result.append(np.mean(data[lower_bound:upper_bound]))\n",
    "    return result\n",
    "\n",
    "slide_window_half = 25\n",
    "df_scores = pd.DataFrame(mesa.scores, columns=['Training', 'Validation', 'Test'])\n",
    "\n",
    "sns.set(style='ticks')\n",
    "sns.set_context('talk', font_scale=0.7)\n",
    "fig = plt.figure(figsize=(12, 3.5))\n",
    "for i in range(df_scores.shape[1]):\n",
    "    ax = plt.subplot(1, 3, i+1)\n",
    "    column = df_scores.columns[i]\n",
    "    view = pd.Series(slide_mean(df_scores[column], slide_window_half))\n",
    "    sns.lineplot(data=view, ax=ax)\n",
    "    start_steps = args.start_steps / args.max_estimators\n",
    "    ax.vlines(start_steps, view.min(), view.max(), color=\"orange\", linestyles='dashed', linewidth=3)\n",
    "    ax.text(start_steps, \n",
    "            (view.min() + view.max()) / 2, \n",
    "            'Start meta-training', \n",
    "            rotation=90, ha='center', va='center', fontsize=12)\n",
    "    ax.set(title=f'{dataset} {column}', \n",
    "           xlabel='# Meta-training episodes', \n",
    "           ylabel=f'{column} AUCPRC')    \n",
    "    ax.grid(axis='y')\n",
    "\n",
    "# fig.suptitle(f'Meta-training on {dataset} dataset')\n",
    "plt.tight_layout(pad=1.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison with other resampling baselines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MESA (k=20)  | train 0.972-0.003 | valid 0.686-0.031 | test 0.732-0.039 | 4-fold CV (10 runs) | ave run time: 0.14s\n",
      "MESA (k=10)  | train 0.972-0.003 | valid 0.625-0.046 | test 0.667-0.049 | 4-fold CV (10 runs) | ave run time: 0.07s\n",
      "MESA (k=5)   | train 0.927-0.039 | valid 0.470-0.033 | test 0.521-0.046 | 4-fold CV (10 runs) | ave run time: 0.03s\n",
      "ORG          | train 0.973-0.003 | valid 0.296-0.031 | test 0.377-0.029 | 4-fold CV (10 runs) | ave run time: 0.02s\n",
      "RUS          | train 0.152-0.017 | valid 0.115-0.013 | test 0.118-0.012 | 4-fold CV (10 runs) | ave run time: 0.00s\n",
      "NM           | train 0.032-0.000 | valid 0.032-0.000 | test 0.032-0.001 | 4-fold CV (10 runs) | ave run time: 0.01s\n",
      "NCR          | train 0.787-0.016 | valid 0.345-0.025 | test 0.351-0.050 | 4-fold CV (10 runs) | ave run time: 0.21s\n",
      "ENN          | train 0.772-0.018 | valid 0.341-0.021 | test 0.365-0.043 | 4-fold CV (10 runs) | ave run time: 0.10s\n",
      "Tomek        | train 0.930-0.012 | valid 0.316-0.025 | test 0.364-0.034 | 4-fold CV (10 runs) | ave run time: 0.10s\n",
      "ALLKNN       | train 0.744-0.014 | valid 0.338-0.024 | test 0.363-0.041 | 4-fold CV (10 runs) | ave run time: 0.27s\n",
      "OSS          | train 0.924-0.016 | valid 0.331-0.033 | test 0.369-0.035 | 4-fold CV (10 runs) | ave run time: 0.18s\n",
      "ROS          | train 0.973-0.003 | valid 0.305-0.049 | test 0.355-0.066 | 4-fold CV (10 runs) | ave run time: 0.02s\n",
      "SMOTE        | train 0.973-0.003 | valid 0.268-0.030 | test 0.310-0.062 | 4-fold CV (10 runs) | ave run time: 0.07s\n",
      "ADASYN       | train 0.973-0.003 | valid 0.255-0.044 | test 0.282-0.047 | 4-fold CV (10 runs) | ave run time: 0.08s\n",
      "BorderSMOTE  | train 0.973-0.003 | valid 0.286-0.040 | test 0.327-0.033 | 4-fold CV (10 runs) | ave run time: 0.09s\n",
      "SMOTEENN     | train 0.536-0.033 | valid 0.256-0.030 | test 0.276-0.037 | 4-fold CV (10 runs) | ave run time: 0.20s\n",
      "SMOTETomek   | train 0.953-0.010 | valid 0.272-0.032 | test 0.307-0.063 | 4-fold CV (10 runs) | ave run time: 0.19s\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from baselines.canonical_resampling import Resample_classifier\n",
    "\n",
    "def evaluate(clf, X_train, y_train, X_valid, y_valid, X_test, y_test, rater):\n",
    "    score_train = rater.score(y_train, clf.predict_proba(X_train)[:,1])\n",
    "    score_valid = rater.score(y_valid, clf.predict_proba(X_valid)[:,1])\n",
    "    score_test = rater.score(y_test, clf.predict_proba(X_test)[:,1])\n",
    "    return [score_train, score_valid, score_test]\n",
    "\n",
    "def stratifiedKFoldTest(clf, clf_name, X, y, X_valid, y_valid, rater,\n",
    "                        n_splits=4, repeat=1, random_state=None):\n",
    "    scores_list, time_list = [], []\n",
    "    for i_repeat in range(repeat):\n",
    "        skf = StratifiedKFold(n_splits=n_splits, random_state=random_state)\n",
    "        for train_index, test_index in skf.split(X, y):\n",
    "            X_train, X_test = X[train_index], X[test_index]\n",
    "            y_train, y_test = y[train_index], y[test_index]\n",
    "            start_time = time.clock()\n",
    "            if clf_name[:4] == 'MESA':\n",
    "                clf.fit(X_train, y_train, X_valid, y_valid, verbose=False)\n",
    "            else:\n",
    "                clf.fit(X_train, y_train)\n",
    "            end_time = time.clock()\n",
    "            time_list.append(end_time - start_time)\n",
    "            scores = evaluate(clf, X_train, y_train, X_valid, y_valid, X_test, y_test, rater)\n",
    "            scores_list.append(scores)\n",
    "    return scores_list, time_list\n",
    "\n",
    "cv_params = {\n",
    "    'X': np.concatenate([X_train, X_test]),\n",
    "    'y': np.concatenate([y_train, y_test]),\n",
    "    'X_valid': X_valid,\n",
    "    'y_valid': y_valid,\n",
    "    'rater': Rater('aucprc'),\n",
    "    'n_splits': 4,\n",
    "    'repeat': 10,\n",
    "}\n",
    "    \n",
    "resample_names = ['ORG', 'RUS', 'NM', 'NCR', 'ENN', \n",
    "    'Tomek', 'ALLKNN', 'OSS',  'ROS', 'SMOTE', 'ADASYN', \n",
    "    'BorderSMOTE', 'SMOTEENN', 'SMOTETomek']\n",
    "resample_clf_list = [Resample_classifier(resample_by=method) for method in resample_names]\n",
    "\n",
    "def copyMesa(mesa, n_estimators=None):\n",
    "    mesa_copy = deepcopy(mesa)\n",
    "    if n_estimators is not None:\n",
    "        mesa_copy.n_estimators = n_estimators\n",
    "    return mesa_copy\n",
    "\n",
    "ensemble_size_list = [20, 10, 5]\n",
    "clf_names = [f'MESA (k={size})' for size in ensemble_size_list] + resample_names\n",
    "clf_list = [copyMesa(mesa, size) for size in ensemble_size_list] + resample_clf_list\n",
    "\n",
    "df_results_list = []\n",
    "for (clf, clf_name) in zip(clf_list, clf_names):\n",
    "    scores_list, time_list = stratifiedKFoldTest(clf, clf_name, **cv_params)\n",
    "    df_results = pd.DataFrame(scores_list, columns=['train', 'valid', 'test'])\n",
    "    df_results['time'] = time_list\n",
    "    df_results['method'] = clf_name\n",
    "    df_results_list.append(df_results)\n",
    "    info = '{:<12s} |'.format(clf_name)\n",
    "    for column in ['train', 'valid', 'test']:\n",
    "        info += ' {} {:.3f}-{:.3f} |'.format(\n",
    "            column, df_results.mean()[column], df_results.std()[column])\n",
    "    info += ' {}-fold CV ({} runs) | ave run time: {:.2f}s'.format(\n",
    "        cv_params['n_splits'], cv_params['repeat'], np.mean(time_list))\n",
    "    print (info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the performance with error bar\n",
    "df_results_all = pd.concat(df_results_list)\n",
    "order_performance = df_results_all.groupby('method').mean()['test'].sort_values(ascending=False).index.tolist()\n",
    "order_runtime = df_results_all.groupby('method').mean()['time'].sort_values(ascending=True).index.tolist()\n",
    "\n",
    "order_performance = order_runtime = clf_names\n",
    "\n",
    "# fig = plt.figure(figsize=(20, 8))\n",
    "sns.set(style='whitegrid')\n",
    "sns.set_context('talk', font_scale=1)\n",
    "# ax = sns.barplot(x='test', y='method', data=df_results_all, ci=\"sd\", capsize=.2, order=order)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 8))\n",
    "sns.stripplot(x='test', y='method', data=df_results_all, order=order_performance, size=4, color=\".3\", ax=ax1)\n",
    "sns.boxplot(x='test', y='method', data=df_results_all, order=order_performance, ax=ax1)\n",
    "ax1.set(xlabel='Test AUCPRC', ylabel='Method')\n",
    "sns.stripplot(x='time', y='method', data=df_results_all, order=order_runtime, size=4, color=\".3\", ax=ax2)\n",
    "sns.boxplot(x='time', y='method', data=df_results_all, order=order_runtime, ax=ax2)\n",
    "ax2.set(xlabel='Run Time', ylabel='Method')\n",
    "\n",
    "fig.suptitle('Results in {} task ({}-fold stratified CV, {} independent runs)'.format(dataset, cv_params['n_splits'], cv_params['repeat']), y=1.05)\n",
    "plt.xlim(0,)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
